April 29, 2021

660 words 4 mins read



Code for Motion Representations for Articulated Animation paper

repo name snap-research/articulated-animation
repo link https://github.com/snap-research/articulated-animation
homepage https://snap-research.github.io/articulated-animation/
language Jupyter Notebook
size (curr.) 48066 kB
stars (curr.) 229
created 2021-03-23
license Other

Motion Representations for Articulated Animation

This repository contains the source code for the CVPR'2021 paper Motion Representations for Articulated Animation by Aliaksandr Siarohin, Oliver Woodford, Jian Ren, Menglei Chai and Sergey Tulyakov.

For more qualitiative examples visit our project page.

Example animation

Here is an example of several images produced by our method. In the first column the driving video is shown. For the remaining columns the top image is animated by using motions extracted from the driving.



We support python3. To install the dependencies run:

pip install -r requirements.txt

YAML configs

There are several configuration files one for each dataset in the config folder named as config/dataset_name.yaml. See config/dataset.yaml to get the description of each parameter.

See description of the parameters in the config/vox256.yaml. We adjust the the configuration to run on 1 V100 GPU, training on 256x256 dataset takes approximatly 2 days.

Pre-trained checkpoints

Checkpoints can be found in checkpoints folder. Checkpoints are large, therefore we use git lsf to store them. Either use git lfs pull or download checkpoints manually from github.

Animation Demo

To run a demo, download a checkpoint and run the following command:

python demo.py  --config config/dataset_name.yaml --driving_video path/to/driving --source_image path/to/source --checkpoint path/to/checkpoint

The result will be stored in result.mp4. To use Animation via Disentaglemet add --mode avd, for standard animation add --mode standard instead.

Colab Demo

We prepared a demo runnable in google-colab, see: demo.ipynb.


To train a model run:

CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --device_ids 0

The code will create a folder in the log directory (each run will create a time-stamped new folder). Checkpoints will be saved to this folder. To check the loss values during training see log.txt. You can also check training data reconstructions in the train-vis subfolder. Then to train Animation via disentaglement (AVD) use:

CUDA_VISIBLE_DEVICES=0 python run.py --checkpoint log/{folder}/cpk.pth --config config/dataset_name.yaml --device_ids 0 --mode train_avd

Where {folder} is the name of the folder created in the previous step. (Note: use backslash ‘' before space.) This will use the same folder where checkpoint was previously stored. It will create a new checkpoint containing all the previous models and the trained avd_network. You can monitor performance in log file and visualizations in train-vis folder.

Evaluation on video reconstruction

To evaluate the reconstruction performance run:

CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode reconstruction --checkpoint log/{folder}/cpk.pth

Where {folder} is the name of the folder created in the previous step. (Note: use backslash ‘' before space.) The reconstruction subfolder will be created in the checkpoint folder. The generated video will be stored to this folder, also generated videos will be stored in png subfolder in loss-less ‘.png’ format for evaluation. Instructions for computing metrics from the paper can be found here.

TED dataset

For obtaining TED dataset run the following commands:

git clone https://github.com/AliaksandrSiarohin/video-preprocessing
cd video-preprocessing
python load_videos.py --metadata ../data/ted384-metadata.csv --format .mp4 --out_folder ../data/TED384-v2 --workers 8 --image_shape 384,384

Training on your own dataset

  1. Resize all the videos to the same size, e.g 256x256, the videos can be in ‘.gif’, ‘.mp4’ or folder with images. We recommend the latter, for each video make a separate folder with all the frames in ‘.png’ format. This format is loss-less, and it has better i/o performance.

  2. Create a folder data/dataset_name with 2 subfolders train and test, put training videos in the train and testing in the test.

  3. Create a config file config/dataset_name.yaml. See description of the parameters in the config/vox256.yaml. Specify the dataset root in dataset_params specify by setting root_dir: data/dataset_name. Adjust other parameters as desired, such as the number of epochs for example. Specify id_sampling: False if you do not want to use id_sampling.

Additional notes


        author={Siarohin, Aliaksandr and Woodford, Oliver and Ren, Jian and Chai, Menglei and Tulyakov, Sergey},
        title={Motion Representations for Articulated Animation},
        booktitle = {CVPR},
        year = {2021}
comments powered by Disqus